Wang Yuanchao1; Li Mingtao1; Pan Zhichen2; Zheng Jianhua1
Source Publicationresearchinastronomyandastrophysics
AbstractAs the performance of dedicated facilities has continually improved, large numbers of pulsar candidates are being received, which makes selecting valuable pulsar signals from the candidates challenging. In this paper, we describe the design for a deep convolutional neural network (CNN) with 11 layers for classifying pulsar candidates. Compared to artificially designed features, the CNN chooses the subintegrations plot and sub-bands plot for each candidate as inputs without carrying biases. To address the imbalance problem, a data augmentation method based on synthetic minority samples is proposed according to the characteristics of pulsars. The maximum pulses of pulsar candidates were first translated to the same position, and then new samples were generated by adding up multiple subplots of pulsars. The data augmentation method is simple and effective for obtaining varied and representative samples which keep pulsar characteristics. In experiments on the HTRU 1 dataset, it is shown that this model can achieve recall of 0.962 and precision of 0.963.
Document Type期刊论文
Recommended Citation
GB/T 7714
Wang Yuanchao,Li Mingtao,Pan Zhichen,et al. pulsarcandidateclassificationwithdeepconvolutionalneuralnetworks[J]. researchinastronomyandastrophysics,2019,19(9).
APA Wang Yuanchao,Li Mingtao,Pan Zhichen,&Zheng Jianhua.(2019).pulsarcandidateclassificationwithdeepconvolutionalneuralnetworks.researchinastronomyandastrophysics,19(9).
MLA Wang Yuanchao,et al."pulsarcandidateclassificationwithdeepconvolutionalneuralnetworks".researchinastronomyandastrophysics 19.9(2019).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang Yuanchao]'s Articles
[Li Mingtao]'s Articles
[Pan Zhichen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang Yuanchao]'s Articles
[Li Mingtao]'s Articles
[Pan Zhichen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang Yuanchao]'s Articles
[Li Mingtao]'s Articles
[Pan Zhichen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.